Obtaining three-dimensional models of scanned objects, also called three-dimensional reconstruction, has been a major focus in the fields of computer vision and robotics during the last decades. The launch of low-cost RGB-D sensors (Red-Green-Blue and Depth), which capture images accompanied with depth information, has boosted the interest in three-dimensional reconstruction. RGB-D sensors enable the development of methods delivering colored, metrically accurate reconstructions. These RGB-D sensors have become more and more important for many different fields such as manufacturing verification, gaming, robotics, object recognition, and object manipulation.
Methods for reconstructing three-dimensional models include in-hand methods, wherein an object facing a camera is carried by hand. This method requires intricate background segmentation in order to separate the object from the background. It has been proposed to solve this issue by the use of colored gloves on the hands of a user carrying the object. In-hand set-ups have proved to be reliable for objects having a distinctive appearance and small movements between frames. However, in-hands set-ups often fail to process objects with light geometrical and textural features because the determination of the transformation between frames is unreliable.
The use of robot arms has thus been proposed to support the object and to register arm poses to obtain the visual transformation that transforms a frame into another frame.
Alternatively, stationary set-ups have also been proposed, wherein the object to be scanned or reconstructed is placed on top of a rotating support surface. These set-ups may use markers on the support surface or they may even be founded on the use of sufficiently textured support surfaces. The use of a support surface having markers makes the segmentation easier and allows using stationary set-ups for any object without appearance constraints for the object.
One known method for reconstructing models is Microsoft's Kinectfusion™. This method employs an RGB-D sensor and a method known to the skilled man under the acronym ICP (Iterative Closest Point). Kinectfusion™ often fails in the reconstruction of models of various objects. More precisely, objects having symmetrical features or non-distinctive geometry are poorly reconstructed by Kinectfusion™. Kinectfusion™ also requires the user to specify volume boundaries and to participate in post-processing steps.
It should be noted that there is a trend towards systems that provide a good level of automation, and that reconstruct models of objects without requiring a thorough supervision by a user.
It is a primary object of the disclosure to provide methods and system that overcome the deficiencies of the currently available systems and methods.